The Internet has presented the ability to collect enormous amounts of data that was previously unattainable from just offline sources. This data provides rich information useful for various analytics, marketing, and other purposes. For instance, in the context of digital marketing, customer data can be used to build predictive models, providing marketers with predictive capabilities, such as identifying valuable customers or estimating likelihood that a product will be purchased.
Often, a large amount of data is available that can include hundreds of features. Exploring the data can be a very difficult and time consuming process given such a large number of features. One approach to working with datasets having a large number of features is to use classification to organize the features. For instance, semantic classification of features can be performed by classifying features in pre-defined semantic classes. This could be performed, for instance, using a dictionary-based approach in which a dictionary maps terms to each semantic class. Features are classified by a lookup in the dictionary using the feature names. However, this approach fails to classify features when a lookup in the dictionary for the feature names fails to find a match. As a result, such dictionary-based semantic classification results in a set of unclassified features. In some instances, this could be a large portion of the features.